tfx.components.ExampleValidator

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A TFX component to validate input examples.

Inherits From: BaseComponent, BaseNode

View aliases

Main aliases

tfx.components.example_validator.component.ExampleValidator

tfx.components.ExampleValidator(
    statistics: tfx.types.Channel = None,
    schema: tfx.types.Channel = None,
    exclude_splits: Optional[List[Text]] = None,
    anomalies: Optional[Text] = None,
    instance_name: Optional[Text] = None
)

Used in the notebooks

Used in the tutorials
TFX Estimator Component Tutorial TFX Keras Component Tutorial Graph-based Neural Structured Learning in TFX Data validation using TFX Pipeline and TensorFlow Data Validation Feature Engineering using TFX Pipeline and TensorFlow Transform

The ExampleValidator component uses Tensorflow Data Validation to validate the statistics of some splits on input examples against a schema.

The ExampleValidator component identifies anomalies in training and serving data. The component can be configured to detect different classes of anomalies in the data. It can:

• perform validity checks by comparing data statistics against a schema that codifies expectations of the user.

Schema Based Example Validation The ExampleValidator component identifies any anomalies in the example data by comparing data statistics computed by the StatisticsGen component against a schema. The schema codifies properties which the input data is expected to satisfy, and is provided and maintained by the user.

Please see https://www.tensorflow.org/tfx/data_validation for more details.

Example

# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(
    statistics=statistics_gen.outputs['statistics'],
    schema=infer_schema.outputs['schema'])

Args
statistics	A Channel of type standard_artifacts.ExampleStatistics.
schema	A Channel of type standard_artifacts.Schema. required
exclude_splits	Names of splits that the example validator should not validate. Default behavior (when exclude_splits is set to None) is excluding no splits.
anomalies	Output channel of type standard_artifacts.ExampleAnomalies.
instance_name	Optional name assigned to this specific instance of ExampleValidator. Required only if multiple ExampleValidator components are declared in the same pipeline. Either stats or statistics must be present in the arguments.

Attributes
component_id
component_type
downstream_nodes
exec_properties
id	Node id, unique across all TFX nodes in a pipeline. If id is set by the user, return it directly. otherwise, if instance name (deprecated) is available, node id will be: . otherwise, node id will be:
inputs
outputs
type
upstream_nodes

Child Classes

class DRIVER_CLASS

class SPEC_CLASS

Methods

add_downstream_node

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add_downstream_node(
    downstream_node
)

Experimental: Add another component that must run after this one.

This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.

Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.

It is symmetric with add_upstream_node.

Args
downstream_node	a component that must run after this node.

add_upstream_node

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add_upstream_node(
    upstream_node
)

Experimental: Add another component that must run before this one.

This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.

Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.

It is symmetric with add_downstream_node.

Args
upstream_node	a component that must run before this node.

from_json_dict

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@classmethod
from_json_dict(
    dict_data: Dict[Text, Any]
) -> Any

Convert from dictionary data to an object.

get_class_type

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@classmethod
get_class_type() -> Text

get_id

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@classmethod
get_id(
    instance_name: Optional[Text] = None
)

Gets the id of a node.

This can be used during pipeline authoring time. For example: from tfx.components import Trainer

resolver = ResolverNode(..., model=Channel( type=Model, producer_component_id=Trainer.get_id('my_trainer')))

Args
instance_name	(Optional) instance name of a node. If given, the instance name will be taken into consideration when generating the id.

Returns
an id for the node.

to_json_dict

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to_json_dict() -> Dict[Text, Any]

Convert from an object to a JSON serializable dictionary.

with_id

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with_id(
    id: Text
) -> "BaseNode"

with_platform_config

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with_platform_config(
    config: message.Message
) -> "BaseComponent"

Attaches a proto-form platform config to a component.

The config will be a per-node platform-specific config.

Args
config	platform config to attach to the component.

Returns
the same component itself.

Class Variables
EXECUTOR_SPEC	Instance of tfx.dsl.components.base.executor_spec.ExecutorClassSpec